Error timeout: what to do when hy3 fails

A timeout means the client gave up before the response arrived. Streaming LLM requests hit this most often, because the model thinks before the first token and that thinking phase produces no bytes.

A timeout is not an HTTP status code but a client-side wait limit. The request usually reached the gateway and was forwarded upstream, yet the model did not produce a result within the time your client allows. Long context, long output, and non-streaming calls are the three most common triggers.

hy3 is served by Tencent. Everything on this page — triggers, fixes and measured data — is compiled from the real runtime behaviour of this model at the gateway layer.

At this gateway, the most common trigger is: The client timeout is set too low. The recommended first action is: Raise the client timeout to 300 seconds or more.

Common causes

  • The client timeout is set too low
  • A long prompt or context pushes first-token latency up
  • The model is working on a long reasoning task
  • Network jitter

How to fix

  • Raise the client timeout to 300 seconds or more
  • Enable streaming so you do not wait for the whole answer
  • Shorten the context or use a faster model
  • Lower max_tokens

Retry with exponential backoff

The snippet below retries when hy3 returns timeout, up to 5 attempts, with an increasing wait plus random jitter so concurrent calls do not retry in lockstep. Read the base URL and API key from environment variables — never hardcode them.

import os, time, random
import requests

BASE  = os.getenv("OPENAI_BASE_URL")   # e.g. https://<your-gateway>/v1
KEY   = os.getenv("OPENAI_API_KEY")
MODEL = 'hy3'


def chat(messages, retries=5):
    """Retry with exponential backoff + jitter."""
    for i in range(retries):
        try:
            r = requests.post(
                BASE + "/chat/completions",
                headers={"Authorization": "Bearer " + KEY},
                json={"model": MODEL, "messages": messages, "stream": True},
                timeout=60,
            )
            if r.status_code == 429 or r.status_code >= 500:
                time.sleep(min(2 ** i + random.uniform(0, 1), 30))
                continue
            r.raise_for_status()
            return r.json()
        except requests.exceptions.Timeout:
            time.sleep(min(2 ** i + random.uniform(0, 1), 30))
    raise RuntimeError("gave up after " + str(retries) + " retries")


print(chat([{"role": "user", "content": "hello"}]))

Key facts for this model

API endpointhttps://api.airai.cc/v1
OpenAI-compatibleOpenAI-compatible
VendorTencent
Context256K
CapabilitiesReasoning, Tools, Open Weights
API formatsopenai, openai-response, openai-response-compact, anthropic, gemini, openai-alpha-search
Billing formulap * 0 + cr * 0 + cc * 0 + c * 0

FAQ

Should concurrent requests be queued or rate-limited directly?

Concurrency and timeouts are the real variables here, not the model itself. Lower the concurrency first — most throughput complaints disappear once you do. A 256K context means long inputs add noticeably to first-token latency. Start with low concurrency, watch it for a few minutes, then scale up. Use batching or a queue to smooth peaks — steadier than raising concurrency on the fly.

Does switching to a smaller model reduce timeout?

Keep a fallback model ready as well. This model from Tencent has several upstream nodes the gateway can switch between. Keep a lighter fallback model ready so the main flow never breaks. Put the model name in config, so switching upstreams needs no code change.

Do I need to upgrade my plan to fix timeout on hy3?

It is mainly a quota matter, not a fault in the model itself. Raising the plan ceiling or lowering the call rate both help. With billing p * 0 + cr * 0 + cc * 0 + c * 0, failed requests are not counted toward usage. Check your balance and rate limits in the console before debugging code.

Will I be charged when hy3 returns timeout?

No charge — only output actually produced counts toward usage. Only the output already produced is billed; the failed part is not. Billing follows p * 0 + cr * 0 + cc * 0 + c * 0, so no output means no charge. With billing p * 0 + cr * 0 + cc * 0 + c * 0, the cost of long output comes mostly from output tokens. Check your balance and rate limits in the console before debugging code. When estimating cost from p * 0 + cr * 0 + cc * 0 + c * 0, include the retry budget.

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Data updated: 2026-10-10 15:55

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